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Coinlight Text Stats & Token Estimator
x402
Coinlight Text Stats & Token Estimator is a paid API for AI agents from coinlight-app.vercel.app, paid per call via x402, $0.005/call, status unknown (last checked 2026-10-29).
Counts characters, words, lines, and bytes in a text string, and estimates LLM token consumption to help agents budget context windows before making API calls.
Price / call$0.005
Success—
StatusUnknown
How this service works
Coinlight charges USDC per API call with the x402 protocol on Solana and Base.
Output
Returns a JSON object with numeric counts for characters, words, lines, and bytes in the input text, along with an estimated LLM token count — enabling agents to make informed decisions about context window usage before dispatching text to a language model.
Example use cases
Pre-flight prompt token check
“I'm about to send this system prompt to GPT-4 — can you tell me how many tokens it would use so I know if it fits in an 8k context window? Here's the prompt text: 'You are a helpful assistant specialized in financial analysis. Your role is to...'”
Document size audit for RAG pipeline
“I need to know the character count, word count, and estimated token count for this chunk of text I'm about to embed: 'Retrieval-augmented generation (RAG) is a technique that combines information retrieval with language model generation...'”
Validating user input length before LLM call
“Can you measure this user-submitted message and tell me how many tokens it is so I can decide whether to truncate it before sending to the API? The message is: 'I have a very long story to tell you about my experience with the product last week when I visited the store and spoke to the manager...'”
Technical details
Endpoint
GEThttps://coinlight-app.vercel.app/p/text-stats?utm_source=zero.xyzWhen to prefer
Use this endpoint when an AI agent needs to programmatically verify whether a piece of text will fit within an LLM's context window before making a potentially expensive or failing API call. Ideal for pipelines that process variable-length user input, documents, or generated content and need to gate or truncate based on token budget. Prefer this over manual estimation when precision matters or when automating document chunking logic.
Side effects
- Text exceeding 200k characters returns an error
- Empty text input may return zero counts or validation error
- Extremely long inputs may time out
- Token estimates are approximations and may differ slightly from actual tokenizer counts
- Missing required 'text' query parameter returns a 400-style error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"endpoint": "Text stats and token estimate"
}
}Tags
x402
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